Evaluating material circular efficacy of ABM waste-management scenarios using PV ICE

This Jupyter journal uses results from Walzberg et al.'s PV ABM and inputs them into PV ICE, exploring the effect of increasing CE EoL pathway rates, recycling efficiency, and module reliability.

Steps include:

  1. Import libraries and create test folder for simulations
  2. Create simulations and scenarios
  3. Modify parameters
    1. Current Recovery (S1)
    2. Ideal Recovery (S2)
    3. Reliability Same New Installs (S3)
    4. Reliability Maintaining Capacity (S4)
  4. Run mass flow calculations
  5. Compile results
  6. Plotting results
  7. Graphing ABM outputs
  8. Results validation

Simulation Descriptions:

1. Import libraries and create test folder for simulations

2. Create simulations and scenarios

Modify Parameters

Current Recovery (r1)

What is the effect of different reuse, recycle, and repair rates using PV ICE default recycling values?

For r1_better_lifetime, modify same parameters as above, while also modifying scenario reliability inputs: mod_lifetime, mod_reliability_t50, & mod_reliability_t90

Ideal Recovery (r2)

What is the effect of different reuse, recycle, and repair rates when FRELP recycling efficiencies are used and all recycled material is closed-loop?

For r2_better_lifetime, modify same parameters as in r2, while also modifying scenario reliability inputs as in r1_better_lifetime: mod_lifetime, mod_reliability_t50, & mod_reliability_t90

Reliability Same New Installs (r3)

What is the effect of module reliability when recycle rates, reuse rates, repair rates, and recycling efficiencies are changed for select scenarios with different repair bins? Using the same new installs (from NREL's 2021 Electrification Futures Study) as inputs.

Reliability Maintaining Capacity (r4)

What is the effect on cumulative new installs and yearly virgin material demand for different repair bins, when capacity is set for all repair bins at bin A's capacity?

Run mass flow calculations

Compile results

Plotting results

Plotting with USyearly and UScum data frames: r1

Plotting with USyearly and UScum data frames: r2

Plotting with USyearly and UScum data frames: r3

Plotting with USyearly and UScum data frames: r4A-r4D

Graphing ABM outputs

Results validation

Rerun r2 with Julien's new installs and recovery rates and see results

Plots not used in Paper/Poster: